Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics

The next generation of particle physics experiments will face an era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing these data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning, silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing toward a successful transition to the data frontier of fundamental science.

Authors

Institutions

Publication Details

Journal
PRX Intelligence
Published
2026-10-13
DOI
https://doi.org/10.1103/bjx3-vlj8
Primary Topic
Particle Detector Development and Performance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics

Ryan Kastner, T. Lam, Angela Di Fulvio, H. Ozaki et al.
PRX Intelligence
Particle Detector Development and Performance
article

Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics

Ryan Kastner, T. Lam, Angela Di Fulvio, H. Ozaki, Carl R. Grace, Kyoungchul Kong, Talal Ahmed Chowdhury, C. Madrid, K. Ishidoshiro, T. Braine, Maryam Bayat Makou, Jennet Elizabeth Dickinson, Doojin Kim, Bo-Cheng Charles Lai, Victoria Nguyen, Miaochen Jin, N. Akchurin, K. F. Di Petrillo, Myeonghun Park, Marco Carminati, L. Gouskos, S. Pagan Griso, Ema Puljak, Vladimir Lončar, L. Gastaldo, S. B. Oh, S. Parajuli, Andreas Werner Jung, Jeffrey Lazar, Zhenghua Ma, G. Karagiorgi, K. Choi, Seokju Chung, Erdem Yigit Ertorer, A. Tapper, Ryan T. Herbst, N. Kamp, Giuseppe Di Guglielmo, Y.-T. Lai, Shiva Abbaszadeh, Larry L. Ruckman, Abhilasha Dave, Lino Gerlach, Audrey Corbeil Therrien, Prashansa Mukim, Benjamin Ramhorst, Sara Pozzi, Javier Mauricio Duarte, J. P. Johnson, S. Bhattacharya, Ekaterina Govorkova, T. Heim, Y. Feng, Christoph Paus, Ciaran Hasnip, C. Boutan, Alberto Coppi, K. Yoshihara, Lorenzo Borella, H. Xu, Cristián Peña, Carlos Arguelles, Seon-Hee, Antonio Chahine, Haoyun Liu, David C. Miller, Katie Kudela, Mark S. Neubauer, Jenni Ott, Emmett Forrestel, Seo, Chance Desmet, Julia Gonski, Abraham Thomas Holtermann, Sagar Addepalli, Tae Min Hong, Auralee Edelen, Amy Roberts, Christian Herwig, Jan Eysermans, Dylan Rankin, Benedikt Maier, Lindsey Gray, Matteo Cremonesi, Stephen Jones, Luca Macchiarulo, James Brau, Zepeng Li, Philip Harris, Qibin Liu, Ryan Coffee, Mariarosaria D’Alfonso, Christopher Grant, Abarajithan Gnaneswaran, Julián García Pardiñas, Gian Michele Innocenti, Benjamin Parpillon, Isobel Ojalvo, Jelena Lalic
article en

Abstract

The next generation of particle physics experiments will face an era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing these data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning, silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing toward a successful transition to the data frontier of fundamental science.

PRX IntelligenceVol. 1(2)
Boston University (US), Northwestern University (US), University of Hawaiʻi at Mānoa (US), Seoul National University of Science and Technology (KR), Texas Tech University (US), Fermi National Accelerator Laboratory (US), Université de Sherbrooke (CA), Harvard University (US), University of Padua (IT), High Energy Accelerator Research Organization (JP), Pacific Northwest National Laboratory (US), University of Kansas (US), Lawrence Berkeley National Laboratory (US), National Yang Ming Chiao Tung University (TW), Istituto Nazionale di Fisica Nucleare (IT), Tokyo University of Science (JP), University of Pittsburgh (US), Southern Methodist University (US), University of Oregon (US), University of California, Santa Cruz (US), University of South Dakota (US), Brookhaven National Laboratory (US), Princeton University (US), University of Washington (US), Tohoku University (JP), Cornell University (US), Purdue University West Lafayette (US), Heidelberg University (DE), Brown University (US), University of Michigan (US), SLAC National Accelerator Laboratory (US), ETH Zurich (CH), University of California San Diego (US), University of Chicago (US), Nalu Scientific (United States) (US), Imperial College London (GB), Massachusetts Institute of Technology (US), European Organization for Nuclear Research (CH), Carnegie Mellon University (US), Columbia University (US), University of Pennsylvania (US), Virginia Tech (US), University of Colorado Denver (US), Politecnico di Milano (IT), UCLouvain (BE)
Openalex Percentile: Top 84%
Particle Detector Development and Performance
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.